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銀行業務におけるAIの世界市場規模：製品別、用途別、地域範囲別、予測

Global AI in Banking Market Size By Product (Hardware, Software, Services), By Application (Analytics, Chatbots, Robotic Process Automation (RPA)), By Geographic Scope and Forecast

出版日: 2025年05月08日
発行: Verified Market Research
ページ情報: 英文 202 Pages
納期: 2～3営業日

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概要

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銀行業務におけるAI市場規模と予測

銀行業務におけるAI市場規模は、2024年に116億2,000万米ドルと評価され、2026～2032年にかけてCAGR 32.36%で成長し、2032年には909億7,000万米ドルに達すると予測されます。

銀行業務におけるAIとは、業務効率、顧客体験、意思決定能力を向上させるために、様々な銀行業務に人工知能技術を統合することです。銀行業務における人工知能（AI）用途には、先進的データ分析、自然言語処理（NLP）、機械学習（ML）、ロボットによるプロセス自動化（RPA）などが含まれます。

最も重要な用途の1つは、AIシステムが大量の取引データを分析して疑わしい動向を発見し、潜在的なリスクをリアルタイムで警告する不正行為の検出と防止です。これにより、銀行は金融損失を削減し、顧客を詐欺から守ることができます。

技術の進歩に伴い、銀行業務におけるAIの応用は今後拡大し、自動化やカスタマイズがさらに進むと予測されています。AIのデータ分析能力により、銀行は個々の顧客の要望や嗜好に基づき、高度にパーソナライズされた金融商品やサービスを提供できるようになると考えられます。

世界の銀行業務におけるAI市場力学

世界の銀行業務におけるAI市場を形成している主要市場力学は以下の通りです。

主要市場促進要因

不正検知とリスク管理に対する需要の増加：金融犯罪が複雑化し頻発する中、銀行は不正行為をリアルタイムで検知するためにAIを活用したソリューションに注目しています。膨大な量の取引データを分析し、パターンを見つけ、異常にフラグを立てるAIの能力は、リスク軽減に不可欠なツールとなっています。

パーソナライゼーションによる顧客体験の向上：人工知能（AI）は、銀行部門における顧客サービスの向上に重要な役割を果たしています。銀行は、AIを搭載したチャットボット、バーチャルアシスタント、パーソナライズされた提案を利用することで、消費者にオーダーメイドのソリューションを提供することができます。銀行はAIを使って消費者の行動、嗜好、取引履歴をモニターし、金融商品やサービスを個々のニーズに合わせてカスタマイズすることができます。

業務の効率化とコスト削減：AI技術は、ローン申込処理、書類確認、顧客サービスなど、日常的で反復的なプロセスを自動化することで銀行を支援します。自動化により、人との対話の必要性が減り、手続きが迅速化され、エラーの可能性が低くなります。手続きを合理化することで、AIは運用コストを削減し、銀行はリソースをより効率的に配分し、より価値の高い業務に集中できるようになります。

主要課題

データのプライバシーとセキュリティ銀行が膨大な量の顧客データを分析するためにAIを利用する機会が増えるにつれ、こうした機密情報のプライバシーとセキュリティを保護することが重要になっています。欧州では一般データ保護規則（GDPR）、米国ではカリフォルニア州消費者プライバシー法（CCPA）などの規制遵守が大きなハードルとなっています。

レガシーシステムとの統合：多くの銀行では、最新のAI技術と互換性のないレガシーシステムを使用しています。旧式のインフラにAIソリューションを統合するのは複雑でコストがかかり、業務に支障をきたす可能性があります。

人材の不足：AI技術の急速な拡大には、データサイエンス、機械学習、AI用途に精通した人材が必要です。しかし、こうしたセグメントの人材はかなり不足しており、銀行が有能な従業員を見つけ、確保することは困難です。このような人材格差は、AIの導入と運用の成功を阻害し、銀行がAIをうまく活用する能力を制限する可能性があります。

主要動向

顧客体験の向上：銀行は、個別化された顧客体験を提供するために、ますますAIを活用するようになっています。AIを搭載したチャットボットやバーチャルアシスタントは、問い合わせや取引を簡単に処理する24時間対応のカスタマーサービスを提供するために活用されています。顧客データを評価することで、銀行は商品の提案やサービスをパーソナライズし、顧客の幸福度とロイヤルティを高めることができます。

不正の検出と防止：サイバーリスクの進化に伴い、AI技術は銀行のセキュリティ手順の改善においてますます重要な役割を果たしています。機械学習アルゴリズムはリアルタイムで取引パターンを評価し、不正のシグナルとなり得る奇妙な行動を検出します。銀行は不正検知を自動化することで、潜在的な脅威に迅速に対応し、財務上の損失を抑え、顧客の信頼を維持することができます。

リスク管理とコンプライアンス：人工知能は、より正確なリスク評価を可能にすることで、銀行のリスク管理業務を変化させています。銀行は先進的分析と予測モデリングを利用して、融資、投資、規制コンプライアンスにおいて起こりうる危険を特定することができます。

AI in banking is the integration of artificial intelligence technologies into various banking operations to improve operational efficiency, client experience, and decision-making abilities. Artificial intelligence (AI) applications in banking include sophisticated data analytics, natural language processing (NLP), machine learning (ML), and robotic process automation (RPA).

One of the most important applications is fraud detection and prevention in which AI systems analyze massive volumes of transactional data to discover suspicious trends and alert potential risks in real time. This enables banks to reduce financial losses and safeguard clients from fraud.

The future application of AI in banking is projected to grow as technology advances, resulting in even greater automation and customisation. AI's data analytics capabilities will allow banks to offer highly personalized financial products and services based on individual client demands and preferences.

Global AI in Banking Market Dynamics

The key market dynamics that are shaping global AI in the banking market include:

Key Market Drivers:

Increasing Demand for Fraud Detection and Risk Management: As financial crimes become more complicated and frequent, banks are turning to AI-powered solutions to detect fraudulent activity in real-time. AI's ability to analyze massive volumes of transactional data, find patterns, and flag anomalies has made it an essential tool for risk mitigation.

Improving Customer Experience with Personalization: Artificial intelligence (AI) plays an important role in improving customer service in the banking sector. Banks may provide bespoke solutions to their consumers by using AI-powered chatbots, virtual assistants, and personalized suggestions. Banks can use AI to monitor consumer behavior, preferences, and transaction histories, allowing them to tailor financial goods and services to individual needs.

Operational Efficiency and Cost Reduction: AI technologies assist banks in automating routine and repetitive processes such as loan application processing, document verification, and customer service. Automation decreases the need for human interaction, speeds up procedures, and lowers the chance of error. By streamlining procedures, AI decreases operating costs allowing banks to allocate resources more efficiently and focus on higher-value activities.

Key Challenges:

Data Privacy and Security: As banks increasingly use AI to analyze massive volumes of client data, protecting the privacy and security of this sensitive information becomes critical. Regulatory compliance such as the General Data Protection Regulation (GDPR) in Europe and the California Consumer Privacy Act (CCPA) in the United States presents substantial hurdles.

Integration with Legacy Systems: Many banks still use legacy systems which may not be compatible with modern AI technologies. Integrating AI solutions with antiquated infrastructure can be complicated and costly, potentially disrupting operations.

Talent Scarcity: The rapid expansion of AI technology needs a workforce proficient in data science, machine learning, and AI applications. However, there is a considerable talent shortage in these disciplines making it difficult for banks to find and keep talented employees. This talent gap can inhibit the successful adoption and administration of AI efforts limiting the bank's capacity to employ AI successfully.

Key Trends:

Enhanced Customer Experience: Banks are increasingly using AI to provide individualized customer experiences. AI-powered chatbots and virtual assistants are being utilized to provide 24-hour customer service handling inquiries and transactions with ease. By evaluating client data, banks can personalize product suggestions and services, increasing customer happiness and loyalty.

Fraud Detection and Prevention: As cyber risks evolve, AI technologies play an increasingly important role in improving bank security procedures. Machine learning algorithms evaluate transaction patterns in real-time to detect odd behavior that could signal fraud. Banks can respond faster to potential threats by automating fraud detection, lowering financial losses, and maintaining customer trust.

Risk Management and Compliance: Artificial intelligence is altering bank's risk management operations by allowing for more accurate risk assessments. Banks can use advanced analytics and predictive modeling to identify possible hazards in lending, investments, and regulatory compliance.

Global AI in Banking Market Regional Analysis

Here is a more detailed regional analysis of the global AI in the banking market:

North America:

North America dominates the worldwide AI banking industry owing to its superior technological infrastructure and early adoption of AI solutions by key financial institutions. This supremacy is mostly fueled by the United States, which accounts for the majority of AI investments in the banking industry. The need for improved customer experience and personalization has been a major driver of AI adoption in North American banking.

According to Federal Reserve research, 76% of Americans would use mobile banking apps in 2024, up from 65% in 2020, creating a favorable environment for AI-powered personalized services. According to the American Bankers Association (ABA), 71% of banks are now employing or planning to use artificial intelligence to improve customer service.

According to a Thomson Reuters analysis, regulatory compliance costs US financial companies USD 270 Billion each year. AI is viewed as a critical tool in cost management, with 63% of banks planning to boost their AI investments in regulatory technology by 2025, according to the Financial Stability Board. Gartner predicts that North American banks will invest USD 37.5 Billion in AI technologies by 2025, expanding at a 22.6% CAGR. Government programs promote this expansion, such as the U.S.

Asia Pacific:

The Asia Pacific region is experiencing fastest growth in AI usage in the banking sector owing to rapid digital transformation and increased fintech investments. This rapid expansion is being driven by the region's enormous population, increased internet penetration, and government measures promoting technological breakthroughs in financial services.

The increased desire for tailored financial services and better client experiences is a major driver of AI in banking in the Asia Pacific. According to the Asian Development Bank's (ADB) report, 78% of regional banks intend to deploy AI-driven customization by 2025.

The need for operational efficiency is also driving AI adoption in banking. According to McKinsey & Company, AI technologies have the potential to add up to $1 trillion in value to the global banking industry each year, with Asia-Pacific institutions positioned to benefit significantly. The region's fintech investments have been significant, with KPMG projecting that fintech funding in Asia Pacific will reach USD 50.5 Billion in 2024, up 44% from the previous year. Government assistance has been critical, with efforts such as Singapore's AI Governance Framework and China's New Generation Artificial Intelligence Development Plan promoting AI development.

Global AI in Banking Market: Segmentation Analysis

The Global AI in Banking Market is segmented based on Product, Application, Technology, and Geography.

AI in Banking Market, By Product

Hardware
Software
Services

Based on the Product, the Global AI in Banking Market is bifurcated into Hardware, Software, and Services. The software segment is dominant in the AI banking market driven by the widespread adoption of AI-powered solutions such as fraud detection, risk management, and customer service chatbots. Banks are increasingly relying on advanced software applications to automate complex processes, analyze large datasets, and enhance decision-making accuracy. AI software enables financial institutions to improve operational efficiency, personalize customer experiences, and detect anomalies in real time, which are critical in a competitive banking landscape.

AI in Banking Market, By Application

Analytics
Chatbots
Robotic Process Automation (RPA)

Based on the Application, the Global AI in Banking Market is bifurcated into Analytics, Chatbots, and Robotic Process Automation (RPA). Among the applications of AI in banking, analytics is the dominant segment due to its critical role in enhancing decision-making, risk management, and personalized customer experiences. Banks increasingly rely on AI-driven analytics to process vast amounts of data, identifying patterns, trends, and anomalies that help optimize operations, detect fraud, and assess credit risk more accurately. This data-driven approach enables banks to improve customer targeting, reduce operational costs, and enhance overall efficiency. Additionally, predictive analytics allows for proactive financial planning and portfolio management.

AI in Banking Market, By Geography

North America
Europe
Asia Pacific
Rest of the World

Based on Geography, the Global AI in Banking Market is classified into North America, Europe, Asia Pacific, and the Rest of the World. North America is the dominant region in the AI banking market driven by the rapid adoption of advanced technologies and a highly developed banking infrastructure. Major financial institutions in the U.S. and Canada are leveraging AI for various applications such as fraud detection, personalized banking services, risk management, and customer service automation through AI-powered chatbots. The region's strong emphasis on innovation coupled with significant investments in AI research and development has accelerated the integration of AI in banking operations.

Key Players

The "Global AI in Banking Market" study report will provide valuable insight with an emphasis on the global market. The major players in the market are Intel, Harman International Industries, Cisco Systems, ABB, IBM Corp, Nuance Corporation, Google LLC, Accenture, IPsoft, Inc., Bsh Hausgerate, Hanson Robotics, Blue Frog Robotics, and Fanuc.

Our market analysis also entails a section solely dedicated to such major players wherein our analysts provide an insight into the financial statements of all the major players, along with product benchmarking and SWOT analysis. The competitive landscape section also included as key development strategies, market share, and market ranking analysis of the above-mentioned players globally.

Global AI in Banking Market Key Developments

In November 2024, Amazon Web Services, Inc. announced that the Bank of Ayudhya Public Company Limited (Krungsri) in Thailand will use AWS to boost customer experiences and financial inclusion efforts.
In May 2024, Temenos, a Swiss software company, announced a partnership with Amazon Web Services, Inc. (AWS) to deliver core banking solutions via a Software-as-a-Service (SaaS) paradigm, seamlessly integrating its application with AWS infrastructure.

1 INTRODUCTION OF GLOBAL AI IN BANKING MARKET

1.1 Overview of the Market
1.2 Scope of Report
1.3 Assumptions

2 EXECUTIVE SUMMARY

3 RESEARCH METHODOLOGY OF VERIFIED MARKET RESEARCH

3.1 Data Mining
3.2 Validation
3.3 Primary Interviews
3.4 List of Data Sources

4 GLOBAL AI IN BANKING MARKET OUTLOOK

4.1 Overview
4.2 Market Dynamics
- 4.2.1 Drivers
- 4.2.2 Restraints
- 4.2.3 Opportunities
4.3 Porters Five Force Model
4.4 Value Chain Analysis

5 GLOBAL AI IN BANKING MARKET, BY PRODUCT

5.1 Overview
5.2 Hardware
5.3 Software
5.4 Services

6 GLOBAL AI IN BANKING MARKET, BY APPLICATION

6.1 Overview
6.2 Analytics
6.3 Chatbots
6.4 Robotic Process Automation

7 GLOBAL AI IN BANKING MARKET, BY GEOGRAPHY

7.1 Overview
7.2 North America
- 7.2.1 U.S.
- 7.2.2 Canada
- 7.2.3 Mexico
7.3 Europe
- 7.3.1 Germany
- 7.3.2 U.K.
- 7.3.3 France
- 7.3.4 Italy
- 7.3.5 Spain
- 7.3.6 Rest of Europe
7.4 Asia Pacific
- 7.4.1 China
- 7.4.2 Japan
- 7.4.3 India
- 7.4.4 Rest of Asia Pacific
7.5 Rest of the World
- 7.5.1 Latin America
- 7.5.2 Middle East and Africa

8 GLOBAL AI IN BANKING MARKET COMPETITIVE LANDSCAPE

8.1 Overview
8.2 Company Market Ranking
8.3 Key Development Strategies

9 COMPANY PROFILES

9.1 Intel
- 9.1.1 Company Overview
- 9.1.2 Company Insights
- 9.1.3 Business Breakdown
- 9.1.4 Product Benchmarking
- 9.1.5 Key Developments
- 9.1.6 Winning Imperatives
- 9.1.7 Current Focus & Strategies
- 9.1.8 Threat from Competition
- 9.1.9 SWOT Analysis
9.2 Harman International Industries
- 9.2.1 Company Overview
- 9.2.2 Company Insights
- 9.2.3 Business Breakdown
- 9.2.4 Product Benchmarking
- 9.2.5 Key Developments
- 9.2.6 Winning Imperatives
- 9.2.7 Current Focus & Strategies
- 9.2.8 Threat from Competition
- 9.2.9 SWOT Analysis
9.3 Cisco Systems
- 9.3.1 Company Overview
- 9.3.2 Company Insights
- 9.3.3 Business Breakdown
- 9.3.4 Product Benchmarking
- 9.3.5 Key Developments
- 9.3.6 Winning Imperatives
- 9.3.7 Current Focus & Strategies
- 9.3.8 Threat from Competition
- 9.3.9 SWOT Analysis
9.4 ABB
- 9.4.1 Company Overview
- 9.4.2 Company Insights
- 9.4.3 Business Breakdown
- 9.4.4 Product Benchmarking
- 9.4.5 Key Developments
- 9.4.6 Winning Imperatives
- 9.4.7 Current Focus & Strategies
- 9.4.8 Threat from Competition
- 9.4.9 SWOT Analysis
9.5 IBM Corp
- 9.5.1 Company Overview
- 9.5.2 Company Insights
- 9.5.3 Business Breakdown
- 9.5.4 Product Benchmarking
- 9.5.5 Key Developments
- 9.5.6 Winning Imperatives
- 9.5.7 Current Focus & Strategies
- 9.5.8 Threat from Competition
- 9.5.9 SWOT Analysis
9.6 Nuance Corporation
- 9.6.1 Company Overview
- 9.6.2 Company Insights
- 9.6.3 Business Breakdown
- 9.6.4 Product Benchmarking
- 9.6.5 Key Developments
- 9.6.6 Winning Imperatives
- 9.6.7 Current Focus & Strategies
- 9.6.8 Threat from Competition
- 9.6.9 SWOT Analysis
9.7 Google LLC
- 9.7.1 Company Overview
- 9.7.2 Company Insights
- 9.7.3 Business Breakdown
- 9.7.4 Product Benchmarking
- 9.7.5 Key Developments
- 9.7.6 Winning Imperatives
- 9.7.7 Current Focus & Strategies
- 9.7.8 Threat from Competition
- 9.7.9 SWOT Analysis
9.8 Accenture
- 9.8.1 Company Overview
- 9.8.2 Company Insights
- 9.8.3 Business Breakdown
- 9.8.4 Product Benchmarking
- 9.8.5 Key Developments
- 9.8.6 Winning Imperatives
- 9.8.7 Current Focus & Strategies
- 9.8.8 Threat from Competition
- 9.8.9 SWOT Analysis
9.9 IPsoft, Inc.
- 9.9.1 Company Overview
- 9.9.2 Company Insights
- 9.9.3 Business Breakdown
- 9.9.4 Product Benchmarking
- 9.9.5 Key Developments
- 9.9.6 Winning Imperatives
- 9.9.7 Current Focus & Strategies
- 9.9.8 Threat from Competition
- 9.9.9 SWOT Analysis
9.10 Bsh Hausgerate
- 9.10.1 Company Overview
- 9.10.2 Company Insights
- 9.10.3 Business Breakdown
- 9.10.4 Product Benchmarking
- 9.10.5 Key Developments
- 9.10.6 Winning Imperatives
- 9.10.7 Current Focus & Strategies
- 9.10.8 Threat from Competition
- 9.10.9 SWOT Analysis
9.11 Hanson Robotics
- 9.11.1 Company Overview
- 9.11.2 Company Insights
- 9.11.3 Business Breakdown
- 9.11.4 Product Benchmarking
- 9.11.5 Key Developments
- 9.11.6 Winning Imperatives
- 9.11.7 Current Focus & Strategies
- 9.11.8 Threat from Competition
- 9.11.9 SWOT Analysis
9.12 Blue Frog Robotics
- 9.12.1 Company Overview
- 9.12.2 Company Insights
- 9.12.3 Business Breakdown
- 9.12.4 Product Benchmarking
- 9.12.5 Key Developments
- 9.12.6 Winning Imperatives
- 9.12.7 Current Focus & Strategies
- 9.12.8 Threat from Competition
- 9.12.9 SWOT Analysis
9.13 Fanuc
- 9.13.1 Company Overview
- 9.13.2 Company Insights
- 9.13.3 Business Breakdown
- 9.13.4 Product Benchmarking
- 9.13.5 Key Developments
- 9.13.6 Winning Imperatives
- 9.13.7 Current Focus & Strategies
- 9.13.8 Threat from Competition
- 9.13.9 SWOT Analysis

10 KEY DEVELOPMENTS

10.1 Product Launches/Developments
10.2 Mergers and Acquisitions
10.3 Business Expansions
10.4 Partnerships and Collaborations

11 Appendix

11.1 Related Research

銀行業務におけるAIの世界市場規模：製品別、用途別、地域範囲別、予測

Global AI in Banking Market Size By Product (Hardware, Software, Services), By Application (Analytics, Chatbots, Robotic Process Automation (RPA)), By Geographic Scope and Forecast

銀行業務におけるAI市場規模と予測

世界の銀行業務におけるAI市場力学

目次

第1章世界の銀行業務におけるAI市場の採用

第2章エグゼクティブサマリー

第3章 VERIFIED MARKET RESEARCHの調査手法

第4章世界の銀行業務におけるAI市場展望

第5章銀行向けAIの世界市場：製品別

第6章銀行業務におけるAIの世界市場：用途別

第7章銀行業務におけるAIの世界市場：地域別

第8章世界の銀行業務におけるAI市場の競合情勢

第9章企業プロファイル

第10章主要開発

第11章付録

AI in Banking Market Size and Forecast

Global AI in Banking Market Dynamics

Global AI in Banking Market Regional Analysis

Global AI in Banking Market: Segmentation Analysis

AI in Banking Market, By Product

AI in Banking Market, By Application

AI in Banking Market, By Geography

Key Players

Global AI in Banking Market Key Developments

TABLE OF CONTENTS

1 INTRODUCTION OF GLOBAL AI IN BANKING MARKET

2 EXECUTIVE SUMMARY

3 RESEARCH METHODOLOGY OF VERIFIED MARKET RESEARCH

4 GLOBAL AI IN BANKING MARKET OUTLOOK

5 GLOBAL AI IN BANKING MARKET, BY PRODUCT

6 GLOBAL AI IN BANKING MARKET, BY APPLICATION

7 GLOBAL AI IN BANKING MARKET, BY GEOGRAPHY

8 GLOBAL AI IN BANKING MARKET COMPETITIVE LANDSCAPE

9 COMPANY PROFILES

10 KEY DEVELOPMENTS

11 Appendix

銀行業務におけるAIの世界市場規模：製品別、用途別、地域範囲別、予測

Global AI in Banking Market Size By Product (Hardware, Software, Services), By Application (Analytics, Chatbots, Robotic Process Automation (RPA)), By Geographic Scope and Forecast

銀行業務におけるAI市場規模と予測

世界の銀行業務におけるAI市場力学

目次

第1章 世界の銀行業務におけるAI市場の採用

第2章 エグゼクティブサマリー

第3章 VERIFIED MARKET RESEARCHの調査手法

第4章 世界の銀行業務におけるAI市場展望

第5章 銀行向けAIの世界市場：製品別

第6章 銀行業務におけるAIの世界市場：用途別

第7章 銀行業務におけるAIの世界市場：地域別

第8章 世界の銀行業務におけるAI市場の競合情勢

第9章 企業プロファイル

第10章 主要開発

第11章 付録

AI in Banking Market Size and Forecast

Global AI in Banking Market Dynamics

Global AI in Banking Market Regional Analysis

Global AI in Banking Market: Segmentation Analysis

AI in Banking Market, By Product

AI in Banking Market, By Application

AI in Banking Market, By Geography

Key Players

Global AI in Banking Market Key Developments

TABLE OF CONTENTS

1 INTRODUCTION OF GLOBAL AI IN BANKING MARKET

2 EXECUTIVE SUMMARY

3 RESEARCH METHODOLOGY OF VERIFIED MARKET RESEARCH

4 GLOBAL AI IN BANKING MARKET OUTLOOK

5 GLOBAL AI IN BANKING MARKET, BY PRODUCT

6 GLOBAL AI IN BANKING MARKET, BY APPLICATION

7 GLOBAL AI IN BANKING MARKET, BY GEOGRAPHY

8 GLOBAL AI IN BANKING MARKET COMPETITIVE LANDSCAPE

9 COMPANY PROFILES

10 KEY DEVELOPMENTS

11 Appendix

第1章世界の銀行業務におけるAI市場の採用

第2章エグゼクティブサマリー

第4章世界の銀行業務におけるAI市場展望

第5章銀行向けAIの世界市場：製品別

第6章銀行業務におけるAIの世界市場：用途別

第7章銀行業務におけるAIの世界市場：地域別

第8章世界の銀行業務におけるAI市場の競合情勢

第9章企業プロファイル

第10章主要開発

第11章付録